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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Fri, 10 Dec 2010 16:15:35 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/10/t129199771028z3rsdncsyu7cq.htm/, Retrieved Mon, 29 Apr 2024 09:06:03 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107801, Retrieved Mon, 29 Apr 2024 09:06:03 +0000

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6,3	0,301029996	0,653212514	0,00	0,819543936	1,62324929	3	1	3
2,1	0,255272505	1,838849091	3,41	3,663040975	2,79518459	3	5	4
9,1	-0,15490196	1,431363764	1,02	2,254064453	2,255272505	4	4	4
15,8	0,591064607	1,278753601	-1,64	-0,522878745	1,544068044	1	1	1
5,2	0	1,482873584	2,20	2,227886705	2,593286067	4	5	4
10,9	0,556302501	1,447158031	0,52	1,408239965	1,799340549	1	2	1
8,3	0,146128036	1,698970004	1,72	2,643452676	2,361727836	1	1	1
11	0,176091259	0,84509804	-0,37	0,806179974	2,049218023	5	4	4
3,2	-0,15490196	1,477121255	2,67	2,626340367	2,44870632	5	5	5
6,3	0,322219295	0,544068044	-1,12	0,079181246	1,62324929	1	1	1
6,6	0,612783857	0,77815125	-0,11	0,544068044	1,62324929	2	2	2
9,5	0,079181246	1,017033339	-0,70	0,698970004	2,079181246	2	2	2
3,3	-0,301029996	1,301029996	1,44	2,06069784	2,170261715	5	5	5
11	0,531478917	0,591064607	-0,92	0	1,204119983	3	1	2
4,7	0,176091259	1,612783857	1,93	2,511883361	2,491361694	1	3	1
10,4	0,531478917	0,954242509	-1,00	0,602059991	1,447158031	5	1	3
7,4	-0,096910013	0,880813592	0,02	0,740362689	1,832508913	5	3	4
2,1	-0,096910013	1,653212514	2,72	2,8162413	2,526339277	5	5	5
17,9	0,301029996	1,380211242	-1,00	-0,602059991	1,698970004	1	1	1
6,1	0,278753601	2	1,79	3,120573931	2,426511261	1	1	1
11,9	0,113943352	0,505149978	-1,64	-0,397940009	1,278753601	4	1	3
13,8	0,748188027	0,698970004	0,23	0,799340549	1,079181246	2	1	1
14,3	0,491361694	0,812913357	0,54	1,033423755	2,079181246	2	1	1
15,2	0,255272505	1,079181246	-0,32	1,190331698	2,146128036	2	2	2
10	-0,045757491	1,305351369	1,00	2,06069784	2,230448921	4	4	4
11,9	0,255272505	1,113943352	0,21	1,056904851	1,230448921	2	1	2
6,5	0,278753601	1,431363764	2,28	2,255272505	2,06069784	4	4	4
7,5	-0,045757491	1,255272505	0,40	1,08278537	1,491361694	5	5	5
10,6	0,414973348	0,672097858	-0,55	0,278753601	1,322219295	3	1	3
7,4	0,380211242	0,991226076	0,63	1,702430536	1,716003344	1	1	1
8,4	0,079181246	1,462397998	0,83	2,252853031	2,214843848	2	3	2
5,7	-0,045757491	0,84509804	-0,12	1,089905111	2,352182518	2	2	2
4,9	-0,301029996	0,77815125	0,56	1,322219295	2,352182518	3	2	3
3,2	-0,22184875	1,301029996	1,74	2,243038049	2,178976947	5	5	5
11	0,361727836	0,653212514	-0,05	0,414973348	1,77815125	2	1	2
4,9	-0,301029996	0,875061263	0,30	1,089905111	2,301029996	3	1	3
13,2	0,414973348	0,361727836	-0,98	0,397940009	1,662757832	3	2	2
9,7	-0,22184875	1,380211242	0,62	1,763427994	2,322219295	4	3	4
12,8	0,819543936	0,477121255	0,54	0,591064607	1,146128036	2	1	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107801&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107801&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107801&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 1.15645851462407 + 0.0122857935458461SWS[t] -0.0300141770836811L[t] + 0.123581304657108Wb[t] -0.0371442998271288Wbr[t] -0.397864863256566Tg[t] + 0.0703055296820856P[t] + 0.0500052380746250S[t] -0.226348203385221D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  1.15645851462407 +  0.0122857935458461SWS[t] -0.0300141770836811L[t] +  0.123581304657108Wb[t] -0.0371442998271288Wbr[t] -0.397864863256566Tg[t] +  0.0703055296820856P[t] +  0.0500052380746250S[t] -0.226348203385221D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107801&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  1.15645851462407 +  0.0122857935458461SWS[t] -0.0300141770836811L[t] +  0.123581304657108Wb[t] -0.0371442998271288Wbr[t] -0.397864863256566Tg[t] +  0.0703055296820856P[t] +  0.0500052380746250S[t] -0.226348203385221D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107801&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107801&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 1.15645851462407 + 0.0122857935458461SWS[t] -0.0300141770836811L[t] + 0.123581304657108Wb[t] -0.0371442998271288Wbr[t] -0.397864863256566Tg[t] + 0.0703055296820856P[t] + 0.0500052380746250S[t] -0.226348203385221D[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	1.15645851462407	0.232367	4.9769	2.5e-05	1.2e-05
SWS	0.0122857935458461	0.011712	1.049	0.302561	0.15128
L	-0.0300141770836811	0.12322	-0.2436	0.809212	0.404606
Wb	0.123581304657108	0.067913	1.8197	0.078797	0.039399
Wbr	-0.0371442998271288	0.09297	-0.3995	0.692333	0.346166
Tg	-0.397864863256566	0.103909	-3.829	0.00061	0.000305
P	0.0703055296820856	0.066776	1.0529	0.300815	0.150408
S	0.0500052380746250	0.040704	1.2285	0.228802	0.114401
D	-0.226348203385221	0.082042	-2.7589	0.009786	0.004893

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 1.15645851462407 & 0.232367 & 4.9769 & 2.5e-05 & 1.2e-05 \tabularnewline
SWS & 0.0122857935458461 & 0.011712 & 1.049 & 0.302561 & 0.15128 \tabularnewline
L & -0.0300141770836811 & 0.12322 & -0.2436 & 0.809212 & 0.404606 \tabularnewline
Wb & 0.123581304657108 & 0.067913 & 1.8197 & 0.078797 & 0.039399 \tabularnewline
Wbr & -0.0371442998271288 & 0.09297 & -0.3995 & 0.692333 & 0.346166 \tabularnewline
Tg & -0.397864863256566 & 0.103909 & -3.829 & 0.00061 & 0.000305 \tabularnewline
P & 0.0703055296820856 & 0.066776 & 1.0529 & 0.300815 & 0.150408 \tabularnewline
S & 0.0500052380746250 & 0.040704 & 1.2285 & 0.228802 & 0.114401 \tabularnewline
D & -0.226348203385221 & 0.082042 & -2.7589 & 0.009786 & 0.004893 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107801&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]1.15645851462407[/C][C]0.232367[/C][C]4.9769[/C][C]2.5e-05[/C][C]1.2e-05[/C][/ROW]
[ROW][C]SWS[/C][C]0.0122857935458461[/C][C]0.011712[/C][C]1.049[/C][C]0.302561[/C][C]0.15128[/C][/ROW]
[ROW][C]L[/C][C]-0.0300141770836811[/C][C]0.12322[/C][C]-0.2436[/C][C]0.809212[/C][C]0.404606[/C][/ROW]
[ROW][C]Wb[/C][C]0.123581304657108[/C][C]0.067913[/C][C]1.8197[/C][C]0.078797[/C][C]0.039399[/C][/ROW]
[ROW][C]Wbr[/C][C]-0.0371442998271288[/C][C]0.09297[/C][C]-0.3995[/C][C]0.692333[/C][C]0.346166[/C][/ROW]
[ROW][C]Tg[/C][C]-0.397864863256566[/C][C]0.103909[/C][C]-3.829[/C][C]0.00061[/C][C]0.000305[/C][/ROW]
[ROW][C]P[/C][C]0.0703055296820856[/C][C]0.066776[/C][C]1.0529[/C][C]0.300815[/C][C]0.150408[/C][/ROW]
[ROW][C]S[/C][C]0.0500052380746250[/C][C]0.040704[/C][C]1.2285[/C][C]0.228802[/C][C]0.114401[/C][/ROW]
[ROW][C]D[/C][C]-0.226348203385221[/C][C]0.082042[/C][C]-2.7589[/C][C]0.009786[/C][C]0.004893[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107801&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107801&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	1.15645851462407	0.232367	4.9769	2.5e-05	1.2e-05
SWS	0.0122857935458461	0.011712	1.049	0.302561	0.15128
L	-0.0300141770836811	0.12322	-0.2436	0.809212	0.404606
Wb	0.123581304657108	0.067913	1.8197	0.078797	0.039399
Wbr	-0.0371442998271288	0.09297	-0.3995	0.692333	0.346166
Tg	-0.397864863256566	0.103909	-3.829	0.00061	0.000305
P	0.0703055296820856	0.066776	1.0529	0.300815	0.150408
S	0.0500052380746250	0.040704	1.2285	0.228802	0.114401
D	-0.226348203385221	0.082042	-2.7589	0.009786	0.004893

Multiple Linear Regression - Regression Statistics
Multiple R	0.871289104188098
R-squared	0.759144703076899
Adjusted R-squared	0.694916623897405
F-TEST (value)	11.8195143428682
F-TEST (DF numerator)	8
F-TEST (DF denominator)	30
p-value	2.01849824188471e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.166277661548531
Sum Squared Residuals	0.82944782190144

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.871289104188098 \tabularnewline
R-squared & 0.759144703076899 \tabularnewline
Adjusted R-squared & 0.694916623897405 \tabularnewline
F-TEST (value) & 11.8195143428682 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 30 \tabularnewline
p-value & 2.01849824188471e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.166277661548531 \tabularnewline
Sum Squared Residuals & 0.82944782190144 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107801&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.871289104188098[/C][/ROW]
[ROW][C]R-squared[/C][C]0.759144703076899[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.694916623897405[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.8195143428682[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]30[/C][/ROW]
[ROW][C]p-value[/C][C]2.01849824188471e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.166277661548531[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.82944782190144[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107801&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107801&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R	0.871289104188098
R-squared	0.759144703076899
Adjusted R-squared	0.694916623897405
F-TEST (value)	11.8195143428682
F-TEST (DF numerator)	8
F-TEST (DF denominator)	30
p-value	2.01849824188471e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.166277661548531
Sum Squared Residuals	0.82944782190144

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.301029996	0.119855352382196	0.181174643617804
2	0.255272505	-0.144137473349522	0.399409978349522
3	-0.15490196	-0.0538181140364022	-0.101083845963598
4	0.591064607	0.408574084048084	0.182490522951916
5	0	-0.0409589216577015	0.0409589216577015
6	0.556302501	0.486966018762716	0.0693364822372839
7	0.146128036	0.276122101580857	-0.129994065580857
8	0.176091259	0.0214111866749937	0.154680072325006
9	-0.15490196	-0.120594398861160	-0.0343075611388395
10	0.322219295	0.324305773760966	-0.00208647876096617
11	0.612783857	0.322477484492103	0.290306372507897
12	0.079181246	0.0908704365317088	-0.0116891905317088
13	-0.301029996	-0.134291869693588	-0.166738126306412
14	0.531478917	0.489315513531092	0.0421634034689079
15	0.176091259	0.313752894119091	-0.137661635119091
16	0.531478917	0.256360506593173	0.275118410406827
17	-0.096910013	0.0629675051336751	-0.159877518133675
18	-0.096910013	-0.171156103773061	0.0742460907730612
19	0.301029996	0.451732202679979	-0.150702206679979
20	0.278753601	0.205211496020017	0.0735421049799832
21	0.113943352	0.243017280342579	-0.129073928342579
22	0.748188027	0.838656006320343	-0.0904679793203433
23	0.491361694	0.473129471517794	0.0182322224822059
24	0.255272505	0.161107975692337	0.0941645293076634
25	-0.045757491	-0.0243914682964879	-0.0213660227035121
26	0.255272505	0.504286947065726	-0.249014442065726
27	0.278753601	0.147320816850077	0.131432784149923
28	-0.045757491	0.096591617803765	-0.142349108803765
29	0.414973348	0.244004255152472	0.170969092847528
30	0.380211242	0.443468312121362	-0.0632570701213618
31	0.079181246	0.191380164444305	-0.112198918444305
32	-0.045757491	-0.00211655031076928	-0.0436409406892307
33	-0.301029996	-0.0905723665780947	-0.210457629421905
34	-0.22184875	-0.108686441632607	-0.113162308367393
35	0.361727836	0.280859634606285	0.0808682013937153
36	-0.301029996	-0.146636479279582	-0.154393516720418
37	0.414973348	0.368560885499569	0.0464124625004313
38	-0.22184875	-0.152760524702377	-0.0690882252976226
39	0.819543936	0.85243951344409	-0.0328955774440897

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.301029996 & 0.119855352382196 & 0.181174643617804 \tabularnewline
2 & 0.255272505 & -0.144137473349522 & 0.399409978349522 \tabularnewline
3 & -0.15490196 & -0.0538181140364022 & -0.101083845963598 \tabularnewline
4 & 0.591064607 & 0.408574084048084 & 0.182490522951916 \tabularnewline
5 & 0 & -0.0409589216577015 & 0.0409589216577015 \tabularnewline
6 & 0.556302501 & 0.486966018762716 & 0.0693364822372839 \tabularnewline
7 & 0.146128036 & 0.276122101580857 & -0.129994065580857 \tabularnewline
8 & 0.176091259 & 0.0214111866749937 & 0.154680072325006 \tabularnewline
9 & -0.15490196 & -0.120594398861160 & -0.0343075611388395 \tabularnewline
10 & 0.322219295 & 0.324305773760966 & -0.00208647876096617 \tabularnewline
11 & 0.612783857 & 0.322477484492103 & 0.290306372507897 \tabularnewline
12 & 0.079181246 & 0.0908704365317088 & -0.0116891905317088 \tabularnewline
13 & -0.301029996 & -0.134291869693588 & -0.166738126306412 \tabularnewline
14 & 0.531478917 & 0.489315513531092 & 0.0421634034689079 \tabularnewline
15 & 0.176091259 & 0.313752894119091 & -0.137661635119091 \tabularnewline
16 & 0.531478917 & 0.256360506593173 & 0.275118410406827 \tabularnewline
17 & -0.096910013 & 0.0629675051336751 & -0.159877518133675 \tabularnewline
18 & -0.096910013 & -0.171156103773061 & 0.0742460907730612 \tabularnewline
19 & 0.301029996 & 0.451732202679979 & -0.150702206679979 \tabularnewline
20 & 0.278753601 & 0.205211496020017 & 0.0735421049799832 \tabularnewline
21 & 0.113943352 & 0.243017280342579 & -0.129073928342579 \tabularnewline
22 & 0.748188027 & 0.838656006320343 & -0.0904679793203433 \tabularnewline
23 & 0.491361694 & 0.473129471517794 & 0.0182322224822059 \tabularnewline
24 & 0.255272505 & 0.161107975692337 & 0.0941645293076634 \tabularnewline
25 & -0.045757491 & -0.0243914682964879 & -0.0213660227035121 \tabularnewline
26 & 0.255272505 & 0.504286947065726 & -0.249014442065726 \tabularnewline
27 & 0.278753601 & 0.147320816850077 & 0.131432784149923 \tabularnewline
28 & -0.045757491 & 0.096591617803765 & -0.142349108803765 \tabularnewline
29 & 0.414973348 & 0.244004255152472 & 0.170969092847528 \tabularnewline
30 & 0.380211242 & 0.443468312121362 & -0.0632570701213618 \tabularnewline
31 & 0.079181246 & 0.191380164444305 & -0.112198918444305 \tabularnewline
32 & -0.045757491 & -0.00211655031076928 & -0.0436409406892307 \tabularnewline
33 & -0.301029996 & -0.0905723665780947 & -0.210457629421905 \tabularnewline
34 & -0.22184875 & -0.108686441632607 & -0.113162308367393 \tabularnewline
35 & 0.361727836 & 0.280859634606285 & 0.0808682013937153 \tabularnewline
36 & -0.301029996 & -0.146636479279582 & -0.154393516720418 \tabularnewline
37 & 0.414973348 & 0.368560885499569 & 0.0464124625004313 \tabularnewline
38 & -0.22184875 & -0.152760524702377 & -0.0690882252976226 \tabularnewline
39 & 0.819543936 & 0.85243951344409 & -0.0328955774440897 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107801&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]0.301029996[/C][C]0.119855352382196[/C][C]0.181174643617804[/C][/ROW]
[ROW][C]2[/C][C]0.255272505[/C][C]-0.144137473349522[/C][C]0.399409978349522[/C][/ROW]
[ROW][C]3[/C][C]-0.15490196[/C][C]-0.0538181140364022[/C][C]-0.101083845963598[/C][/ROW]
[ROW][C]4[/C][C]0.591064607[/C][C]0.408574084048084[/C][C]0.182490522951916[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0409589216577015[/C][C]0.0409589216577015[/C][/ROW]
[ROW][C]6[/C][C]0.556302501[/C][C]0.486966018762716[/C][C]0.0693364822372839[/C][/ROW]
[ROW][C]7[/C][C]0.146128036[/C][C]0.276122101580857[/C][C]-0.129994065580857[/C][/ROW]
[ROW][C]8[/C][C]0.176091259[/C][C]0.0214111866749937[/C][C]0.154680072325006[/C][/ROW]
[ROW][C]9[/C][C]-0.15490196[/C][C]-0.120594398861160[/C][C]-0.0343075611388395[/C][/ROW]
[ROW][C]10[/C][C]0.322219295[/C][C]0.324305773760966[/C][C]-0.00208647876096617[/C][/ROW]
[ROW][C]11[/C][C]0.612783857[/C][C]0.322477484492103[/C][C]0.290306372507897[/C][/ROW]
[ROW][C]12[/C][C]0.079181246[/C][C]0.0908704365317088[/C][C]-0.0116891905317088[/C][/ROW]
[ROW][C]13[/C][C]-0.301029996[/C][C]-0.134291869693588[/C][C]-0.166738126306412[/C][/ROW]
[ROW][C]14[/C][C]0.531478917[/C][C]0.489315513531092[/C][C]0.0421634034689079[/C][/ROW]
[ROW][C]15[/C][C]0.176091259[/C][C]0.313752894119091[/C][C]-0.137661635119091[/C][/ROW]
[ROW][C]16[/C][C]0.531478917[/C][C]0.256360506593173[/C][C]0.275118410406827[/C][/ROW]
[ROW][C]17[/C][C]-0.096910013[/C][C]0.0629675051336751[/C][C]-0.159877518133675[/C][/ROW]
[ROW][C]18[/C][C]-0.096910013[/C][C]-0.171156103773061[/C][C]0.0742460907730612[/C][/ROW]
[ROW][C]19[/C][C]0.301029996[/C][C]0.451732202679979[/C][C]-0.150702206679979[/C][/ROW]
[ROW][C]20[/C][C]0.278753601[/C][C]0.205211496020017[/C][C]0.0735421049799832[/C][/ROW]
[ROW][C]21[/C][C]0.113943352[/C][C]0.243017280342579[/C][C]-0.129073928342579[/C][/ROW]
[ROW][C]22[/C][C]0.748188027[/C][C]0.838656006320343[/C][C]-0.0904679793203433[/C][/ROW]
[ROW][C]23[/C][C]0.491361694[/C][C]0.473129471517794[/C][C]0.0182322224822059[/C][/ROW]
[ROW][C]24[/C][C]0.255272505[/C][C]0.161107975692337[/C][C]0.0941645293076634[/C][/ROW]
[ROW][C]25[/C][C]-0.045757491[/C][C]-0.0243914682964879[/C][C]-0.0213660227035121[/C][/ROW]
[ROW][C]26[/C][C]0.255272505[/C][C]0.504286947065726[/C][C]-0.249014442065726[/C][/ROW]
[ROW][C]27[/C][C]0.278753601[/C][C]0.147320816850077[/C][C]0.131432784149923[/C][/ROW]
[ROW][C]28[/C][C]-0.045757491[/C][C]0.096591617803765[/C][C]-0.142349108803765[/C][/ROW]
[ROW][C]29[/C][C]0.414973348[/C][C]0.244004255152472[/C][C]0.170969092847528[/C][/ROW]
[ROW][C]30[/C][C]0.380211242[/C][C]0.443468312121362[/C][C]-0.0632570701213618[/C][/ROW]
[ROW][C]31[/C][C]0.079181246[/C][C]0.191380164444305[/C][C]-0.112198918444305[/C][/ROW]
[ROW][C]32[/C][C]-0.045757491[/C][C]-0.00211655031076928[/C][C]-0.0436409406892307[/C][/ROW]
[ROW][C]33[/C][C]-0.301029996[/C][C]-0.0905723665780947[/C][C]-0.210457629421905[/C][/ROW]
[ROW][C]34[/C][C]-0.22184875[/C][C]-0.108686441632607[/C][C]-0.113162308367393[/C][/ROW]
[ROW][C]35[/C][C]0.361727836[/C][C]0.280859634606285[/C][C]0.0808682013937153[/C][/ROW]
[ROW][C]36[/C][C]-0.301029996[/C][C]-0.146636479279582[/C][C]-0.154393516720418[/C][/ROW]
[ROW][C]37[/C][C]0.414973348[/C][C]0.368560885499569[/C][C]0.0464124625004313[/C][/ROW]
[ROW][C]38[/C][C]-0.22184875[/C][C]-0.152760524702377[/C][C]-0.0690882252976226[/C][/ROW]
[ROW][C]39[/C][C]0.819543936[/C][C]0.85243951344409[/C][C]-0.0328955774440897[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107801&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107801&T=4

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.301029996	0.119855352382196	0.181174643617804
2	0.255272505	-0.144137473349522	0.399409978349522
3	-0.15490196	-0.0538181140364022	-0.101083845963598
4	0.591064607	0.408574084048084	0.182490522951916
5	0	-0.0409589216577015	0.0409589216577015
6	0.556302501	0.486966018762716	0.0693364822372839
7	0.146128036	0.276122101580857	-0.129994065580857
8	0.176091259	0.0214111866749937	0.154680072325006
9	-0.15490196	-0.120594398861160	-0.0343075611388395
10	0.322219295	0.324305773760966	-0.00208647876096617
11	0.612783857	0.322477484492103	0.290306372507897
12	0.079181246	0.0908704365317088	-0.0116891905317088
13	-0.301029996	-0.134291869693588	-0.166738126306412
14	0.531478917	0.489315513531092	0.0421634034689079
15	0.176091259	0.313752894119091	-0.137661635119091
16	0.531478917	0.256360506593173	0.275118410406827
17	-0.096910013	0.0629675051336751	-0.159877518133675
18	-0.096910013	-0.171156103773061	0.0742460907730612
19	0.301029996	0.451732202679979	-0.150702206679979
20	0.278753601	0.205211496020017	0.0735421049799832
21	0.113943352	0.243017280342579	-0.129073928342579
22	0.748188027	0.838656006320343	-0.0904679793203433
23	0.491361694	0.473129471517794	0.0182322224822059
24	0.255272505	0.161107975692337	0.0941645293076634
25	-0.045757491	-0.0243914682964879	-0.0213660227035121
26	0.255272505	0.504286947065726	-0.249014442065726
27	0.278753601	0.147320816850077	0.131432784149923
28	-0.045757491	0.096591617803765	-0.142349108803765
29	0.414973348	0.244004255152472	0.170969092847528
30	0.380211242	0.443468312121362	-0.0632570701213618
31	0.079181246	0.191380164444305	-0.112198918444305
32	-0.045757491	-0.00211655031076928	-0.0436409406892307
33	-0.301029996	-0.0905723665780947	-0.210457629421905
34	-0.22184875	-0.108686441632607	-0.113162308367393
35	0.361727836	0.280859634606285	0.0808682013937153
36	-0.301029996	-0.146636479279582	-0.154393516720418
37	0.414973348	0.368560885499569	0.0464124625004313
38	-0.22184875	-0.152760524702377	-0.0690882252976226
39	0.819543936	0.85243951344409	-0.0328955774440897

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.794814761966986	0.410370476066029	0.205185238033014
13	0.73113047623253	0.537739047534941	0.268869523767470
14	0.606916405966179	0.786167188067641	0.393083594033821
15	0.468723500996101	0.937447001992202	0.531276499003899
16	0.972995053274966	0.0540098934500684	0.0270049467250342
17	0.96632990626222	0.0673401874755584	0.0336700937377792
18	0.945916965442833	0.108166069114335	0.0540830345571674
19	0.930390727311796	0.139218545376408	0.069609272688204
20	0.954628720312776	0.0907425593744486	0.0453712796872243
21	0.93508802365648	0.129823952687039	0.0649119763435194
22	0.883876902739222	0.232246194521556	0.116123097260778
23	0.815227449614288	0.369545100771424	0.184772550385712
24	0.704831623816241	0.590336752367519	0.295168376183759
25	0.5962130916474	0.807573816705201	0.403786908352601
26	0.710466667179001	0.579066665641997	0.289533332820999
27	0.810466455242563	0.379067089514874	0.189533544757437

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.794814761966986 & 0.410370476066029 & 0.205185238033014 \tabularnewline
13 & 0.73113047623253 & 0.537739047534941 & 0.268869523767470 \tabularnewline
14 & 0.606916405966179 & 0.786167188067641 & 0.393083594033821 \tabularnewline
15 & 0.468723500996101 & 0.937447001992202 & 0.531276499003899 \tabularnewline
16 & 0.972995053274966 & 0.0540098934500684 & 0.0270049467250342 \tabularnewline
17 & 0.96632990626222 & 0.0673401874755584 & 0.0336700937377792 \tabularnewline
18 & 0.945916965442833 & 0.108166069114335 & 0.0540830345571674 \tabularnewline
19 & 0.930390727311796 & 0.139218545376408 & 0.069609272688204 \tabularnewline
20 & 0.954628720312776 & 0.0907425593744486 & 0.0453712796872243 \tabularnewline
21 & 0.93508802365648 & 0.129823952687039 & 0.0649119763435194 \tabularnewline
22 & 0.883876902739222 & 0.232246194521556 & 0.116123097260778 \tabularnewline
23 & 0.815227449614288 & 0.369545100771424 & 0.184772550385712 \tabularnewline
24 & 0.704831623816241 & 0.590336752367519 & 0.295168376183759 \tabularnewline
25 & 0.5962130916474 & 0.807573816705201 & 0.403786908352601 \tabularnewline
26 & 0.710466667179001 & 0.579066665641997 & 0.289533332820999 \tabularnewline
27 & 0.810466455242563 & 0.379067089514874 & 0.189533544757437 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107801&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.794814761966986[/C][C]0.410370476066029[/C][C]0.205185238033014[/C][/ROW]
[ROW][C]13[/C][C]0.73113047623253[/C][C]0.537739047534941[/C][C]0.268869523767470[/C][/ROW]
[ROW][C]14[/C][C]0.606916405966179[/C][C]0.786167188067641[/C][C]0.393083594033821[/C][/ROW]
[ROW][C]15[/C][C]0.468723500996101[/C][C]0.937447001992202[/C][C]0.531276499003899[/C][/ROW]
[ROW][C]16[/C][C]0.972995053274966[/C][C]0.0540098934500684[/C][C]0.0270049467250342[/C][/ROW]
[ROW][C]17[/C][C]0.96632990626222[/C][C]0.0673401874755584[/C][C]0.0336700937377792[/C][/ROW]
[ROW][C]18[/C][C]0.945916965442833[/C][C]0.108166069114335[/C][C]0.0540830345571674[/C][/ROW]
[ROW][C]19[/C][C]0.930390727311796[/C][C]0.139218545376408[/C][C]0.069609272688204[/C][/ROW]
[ROW][C]20[/C][C]0.954628720312776[/C][C]0.0907425593744486[/C][C]0.0453712796872243[/C][/ROW]
[ROW][C]21[/C][C]0.93508802365648[/C][C]0.129823952687039[/C][C]0.0649119763435194[/C][/ROW]
[ROW][C]22[/C][C]0.883876902739222[/C][C]0.232246194521556[/C][C]0.116123097260778[/C][/ROW]
[ROW][C]23[/C][C]0.815227449614288[/C][C]0.369545100771424[/C][C]0.184772550385712[/C][/ROW]
[ROW][C]24[/C][C]0.704831623816241[/C][C]0.590336752367519[/C][C]0.295168376183759[/C][/ROW]
[ROW][C]25[/C][C]0.5962130916474[/C][C]0.807573816705201[/C][C]0.403786908352601[/C][/ROW]
[ROW][C]26[/C][C]0.710466667179001[/C][C]0.579066665641997[/C][C]0.289533332820999[/C][/ROW]
[ROW][C]27[/C][C]0.810466455242563[/C][C]0.379067089514874[/C][C]0.189533544757437[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107801&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107801&T=5

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.794814761966986	0.410370476066029	0.205185238033014
13	0.73113047623253	0.537739047534941	0.268869523767470
14	0.606916405966179	0.786167188067641	0.393083594033821
15	0.468723500996101	0.937447001992202	0.531276499003899
16	0.972995053274966	0.0540098934500684	0.0270049467250342
17	0.96632990626222	0.0673401874755584	0.0336700937377792
18	0.945916965442833	0.108166069114335	0.0540830345571674
19	0.930390727311796	0.139218545376408	0.069609272688204
20	0.954628720312776	0.0907425593744486	0.0453712796872243
21	0.93508802365648	0.129823952687039	0.0649119763435194
22	0.883876902739222	0.232246194521556	0.116123097260778
23	0.815227449614288	0.369545100771424	0.184772550385712
24	0.704831623816241	0.590336752367519	0.295168376183759
25	0.5962130916474	0.807573816705201	0.403786908352601
26	0.710466667179001	0.579066665641997	0.289533332820999
27	0.810466455242563	0.379067089514874	0.189533544757437

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	3	0.1875	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 3 & 0.1875 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107801&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.1875[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107801&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107801&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	3	0.1875	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code